检索结果-内蒙古大学图书馆

作者： Wu, Yilun Lin, Xinye Lu, Xicheng Su, Jinshu Chen, Peixin College of Computer National University of Defense Technology China School of Computer Science McGill University Montreal Canada National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology China

Public auditing is a new technique to protect the integrity of outsourced data in the remote cloud. Users delegate the ability of auditing to a third party auditor (TPA), and assume that each result from the TPA is correct. However, the TPA is not always trustworthy in reality. In this paper, we consider a scenario in which the TPA may lower the reputation of the cloud server by cheating users, and propose a novel public auditing scheme to address this security issue. The analyses and the evaluation prove that our scheme is both secure and efficient. © 2016 The Institute of Electronics, Information and Communication Engineers.

关键词： Cloud security

来源：评论

学校读者我要写书评

暂无评论

High-performance Network Traffic Classification Based on Graph Neural Network 6

High-performance Network Traffic Classification Based on Gra...

引用

6th IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2023

作者： Pang, Bo Fu, Yongquan Ren, Siyuan Jia, Yan Harbin Institute of Technology College of Computer Science and Technology Shenzhen China National University of Defense Technology National Key Laboratory for Parallel and Distributed Processing College of Computer Changsha China Peng Cheng Laboratory Shen Zhen China

ISBN: (纸本)9781665460033

Network traffic classification is crucial for network security and network management and is one of the most important network tasks. Current state-of-the-art traffic classifiers are based on deep learning models to automatically extract features from packet streams. Unfortunately, current approaches fail to effectively combine the structural information of traffic packets with the content features of the packets, resulting in limited classification accuracy. In this paper, we propose a graph neural network model for network traffic classification, which can well perceive the interaction feature of packets in traffic. Firstly, we design a graph structure for packets' flows to hold the interaction information between packets, which embeds both packet contents and sequence relationships into a unified graph. Secondly, we propose a graph neural network framework for graph classification to automatically learn the structural features of the packets' flows together with the packets' features. Extensive evaluation results on real-world traffic data show that the proposed model improves the prediction accuracy of improves the prediction accuracy by 2% to 37% for malicious traffic classification. © 2023 IEEE.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

A wikipedia based semantic graph model for topic tracking in blogosphere

A wikipedia based semantic graph model for topic tracking in...

引用

22nd International Joint Conference on Artificial Intelligence, IJCAI 2011

作者： Tang, Jintao Wang, Ting Lu, Qin Wang, Ji Li, Wenjie College of Computer National University of Defense Technology Changsha China Department of Computing Hong Kong Polytechnic University Hong Kong Hong Kong National Laboratory for Parallel and Distributed Processing Changsha China

ISBN: (纸本)9781577355120

There are two key issues for information diffusion in blogosphere: (1) blog posts are usually short, noisy and contain multiple themes, (2) information diffusion through blogosphere is primarily driven by the "word-of- mouth" effect, thus making topics evolve very fast. This paper presents a novel topic tracking approach to deal with these issues by modeling a topic as a semantic graph, in which the semantic relatedness between terms are learned from Wikipedia. For a given topic/post, the name entities, Wikipedia concepts, and the semantic relatedness are extracted to generate the graph model. Noises are filtered out through the graph clustering algorithm. To handle topic evolution, the topic model is enriched by using Wikipedia as background knowledge. Furthermore, graph edit distance is used to measure the similarity between a topic and its posts. The proposed method is tested by using the real-world blog data. Experimental results show the advantage of the proposed method on tracking the topic in short, noisy texts.

关键词： Blogs

来源：评论

学校读者我要写书评

暂无评论

Optimizing Batched Small Matrix Multiplication on Multi-core DSP Architecture 22

Optimizing Batched Small Matrix Multiplication on Multi-core...

引用

22nd IEEE International Symposium on parallel and distributed Processing with Applications, ISPA 2024

作者： Zuo, Xiaohan Xiao, Chunhua Wang, Qinglin Shi, Chen Chongqing University College of Computer Science Chongqing China Ministry of Education Key Laboratory of Dependable Service Computing in Cyber Physical Society China National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing Changsha410073 China National University of Defense Technology Laboratory of Digitizing Software for Frontier Equipment Changsha410073 China

ISBN: (纸本)9798331509712

General Matrix Multiplication (GEMM) is a critical computational operation in scientific computing and machine learning domains. While traditional GEMM performs well on large matrices, it is inefficient in terms of data transfer and computation for small matrices. Many High-Performance computing (HPC) tasks can be decomposed into large batches of small matrix multiplication operations. Multi-core Digital Signal Processors (DSPs) are commonly used to accelerate high-performance computing. We present a design for batched fusion small matrix multiplication (BFMM) tailored for multi-core DSP architecture. To address the inefficiencies and redundancy in storage and computational operations associated with batch small matrix multiplications, we designed several strategies. We design a matrix fusion concatenation strategy, an access coordination mechanism, and a mechanism for fragment aggregation. BFMM supports an efficient K-dimension multi-core parallelization strategy. The parameter constraint model makes BFMM highly portable. BFMM also includes a performance evaluation model that facilitates assessment and verification. Experimental results demonstrate that, compared to traditional GEMM (TGEMM) on multi-core DSP and traditional GEMM with concatenated data access (TGEMM Op), BFMM exhibits superior performance. For large batches of small matrices, our design achieves 1.21x to 18x higher performance than TGEMM Op on single-core DSP, while on multi-core DSP, it outperforms TGEMM Op by 1.14x to 18.1x. © 2024 IEEE.

关键词： Network security

来源：评论

学校读者我要写书评

暂无评论

Simultaneously Learning Syntactic Dependency and Semantics Reasonability for Relation Extraction

Simultaneously Learning Syntactic Dependency and Semantics R...

引用

International Conference on Image, Vision and Intelligent Systems, ICIVIS 2021

作者： Wang, Xin Yin, Nan Zhang, Xiang Bai, Xinyi Luo, Zhigang College of Computer National University of Defense Technology Changsha China Institute for Quantum and State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology Changsha China

ISBN: (纸本)9789811669620

Relation extraction as an important Natural Language Processing (NLP) task is to identify relations between named entities in text. Recently, graph convolutional networks over dependency trees have been widely used to capture syntactic features and achieved attractive performance. However, most existing dependency-based approaches ignore the positive influence of the words outside the dependency trees, sometimes conveying rich and useful information on relation extraction. In this paper, we propose a novel model, Entity-aware Self-attention Contextualized GCN (ESC-GCN), which efficiently incorporates syntactic structure of input sentences and semantic context of sequences. To be specific, relative position self-attention obtains the overall semantic pairwise correlation related to word position, and contextualized graph convolutional networks capture rich intra-sentence dependencies between words by adequately pruning operations. In this way, our proposed model not only reduces the noisy impact from dependency trees but also obtains easily-ignored entity-related semantic representation. Extensive experiments demonstrate that our model achieves encouraging performance. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Character feature learning for named entity recognition

Character feature learning for named entity recognition

引用

作者： Zeng, Ping Tan, Qingping Zhang, Haoyu Meng, Xiankai Zhang, Zhuo Xu, Jianjun Lei, Yan College of Computer National University of Defense Technology Changsha China National Key Laboratory for Parallel and Distributed Processing Changsha China School of Software Engineering Chongqing University Chongqing China

The deep neural named entity recognition model automatically learns and extracts the features of entities and solves the problem of the traditional model relying heavily on complex feature engineering and obscure professional knowledge. This issue has become a hot topic in recent years. Existing deep neural models only involve simple character learning and extraction methods, which limit their capability. To further explore the performance of deep neural models, we propose two character feature learning models based on convolution neural network and long short-term memory network. These two models consider the local semantic and position features of word characters. Experiments conducted on the CoNLL-2003 dataset show that the proposed models outperform traditional ones and demonstrate excellent performance. © Copyright 2018 The Institute of Electronics Information and Communication Engineers.

关键词： Extraction

来源：评论

学校读者我要写书评

暂无评论

parallel computing for large-scale author name disambiguation in medline 21

Parallel computing for large-scale author name disambiguatio...

引用

21st IEEE International Conference on High Performance computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Tang, Anyao Wu, Chengkun Liu, Jie Wang, Wei Yang, Xi Xing, Yuting Science and Technology on Parallel and Distributed Processing Laboratory Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha410073 China State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha410073 China College of Computer National University of Defense Technology Changsha 410073 China

ISBN: (纸本)9781728120584

Author name disambiguation (AND) is an important task in the field of scientific data mining. It has become a great challenge with the rapid growth of academic digital libraries. The task of AND for a large number of authors is computationally intensive. In particular, an author's name in MEDLINE is represented by full last name and initials, like 'Zhang S', which leads to a lot of identical strings that actually represent different names. In this paper, we proposed an efficient algorithm for parallel AND computation. The proposed algorithm mainly addresses the load balancing issue across many computing nodes. It involves the following strategies:(1) Author-based load balancing, which splits the computation load for each core by author name labels. (2) Matrix-based strategy, which calculates the pairwise similarity between publications and saves them in a matrix globally shared by all processes. Then group them by width-first search. We combine the above two strategies, the second of which is used to calculate authors with a large number of documents, and the other authors apply the first. We constructed a publications database written by Chinese authors from MEDLINE, the biggest public database for biomedical literature (abstracts). For benchmark testing, we experimented our algorithm with a dataset of 1 million publications on the Tianhe-2A supercomputer. Firstly, we trained an AND classifier that can achieve 98.1% of F1. The serial computation time is estimated to be approximately 246 hours, while the parallel execution time is approximately 66 hours in the case of four cores on a single node (with a speedup of 3.7x). Finally, we reduced the total parallel computing time of 1 million documents to about 2 hours and achieved 65.8% of parallelism efficiency using 200 cores on 90 nodes. © 2019 IEEE.

关键词： Matrix algebra

来源：评论

学校读者我要写书评

暂无评论

Routing with uncertainty in wireless mesh networks

Routing with uncertainty in wireless mesh networks

引用

2010 IEEE 18th International Workshop on Quality of Service, IWQoS 2010

作者： Chen, Fajun Liu, Jiangchuan Li, Zongpeng Wang, Yijie Key Laboratory of Science and Technology for National Defence of Parallel and Distributed Processing School of Computer National University of Defense Technology China School of Computing Science Simon Fraser University Canada Department of Computer Science University of Caglary Canada

ISBN: (纸本)9781424459889

Existing routing protocols for Wireless Mesh Networks (WMNs) are generally optimized with statistical link measures, while not addressing on the intrinsic uncertainty of wireless links. We show evidence that, with the transient link uncertainties at PHY and MAC layers, a pseudo-deterministic routing protocol that relies on average or historic statistics can hardly explore the full potentials of a multi-hop wireless mesh. We study optimal WMN routing using probing-based online anypath forwarding, with explicit consideration of transient link uncertainties. We show the underlying connection between WMN routing and the classic Canadian Traveller Problem (CTP) [1]. Inspired by a stochastic recoverable version of CTP (SRCTP), we develop a practical SRCTP-based online routing algorithm under link uncertainties. We study how dynamic next hop selection can be done with low cost, and derive a systematic selection order for minimizing transmission delay. We conduct simulation studies to verify the effectiveness of the SRCTP algorithms under diverse network configurations. In particular, compared to deterministic routing, reduction of end-to-end delay (51:15∼73:02%) and improvement on packet delivery ratio (99:76%) are observed.

关键词： Routing protocols

来源：评论

学校读者我要写书评

暂无评论

Multi-level Shared Knowledge Guided Learning for Knowledge Graph Completion

arXiv

引用

arXiv 2024年

作者： Shan, Yongxue Zhou, Jie Peng, Jie Zhou, Xin Yin, Jiaqian Wang, Xiaodong National Key Laboratory of Parallel and Distributed Computing College of Computer National University of Denfense Technology Changsha China

In the task of Knowledge Graph Completion (KGC), the existing datasets and their inherent subtasks carry a wealth of shared knowledge that can be utilized to enhance the representation of knowledge triplets and overall performance. However, no current studies specifically address the shared knowledge within KGC. To bridge this gap, we introduce a multi-level Shared Knowledge Guided learning method (SKG) that operates at both the dataset and task levels. On the dataset level, SKG-KGC broadens the original dataset by identifying shared features within entity sets via text summarization. On the task level, for the three typical KGC subtasks – head entity prediction, relation prediction, and tail entity prediction – we present an innovative multi-task learning architecture with dynamically adjusted loss weights. This approach allows the model to focus on more challenging and underperforming tasks, effectively mitigating the imbalance of knowledge sharing among subtasks. Experimental results demonstrate that SKG-KGC outperforms existing text-based methods significantly on three well-known datasets, with the most notable improvement on WN18RR (MRR: 66.6%→72.2%, Hit@1: 58.7%→67.0%). Copyright © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

来源：评论

学校读者我要写书评

暂无评论

Funnel: An Efficient Sparse Attention Accelerator with Multi-Dataflow Fusion

Funnel: An Efficient Sparse Attention Accelerator with Multi...

引用

International Symposium on parallel and distributed Processing with Applications, ISPA

作者： Shenghong Ma Jinwei Xu Jingfei Jiang Yaohua Wang Dongsheng Li National Key Laboratory of Parallel and Distributed Computing College of Computer National University of Defense Technology Changsha China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

The self-attention mechanism is the core component of Transformer, which provides a powerful ability to understand the sequence context. However, the self-attention mechanism also suffers from a large amount of redundant computation. Model sparsification can effectively reduce computational load, but the irregularity of non-zeros introduced by sparsification significantly decreases hardware efficiency. This paper proposes Funnel, an accelerator that dynamically predicts sparse attention patterns and efficiently processes unstructured sparse data. Firstly, we adopt a fast quantization method based on lookup table to minimize the cost of sparse patterns prediction. Secondly, we propose Funnel computing Unit (FCU), a hardware architecture that efficiently handles sparse attention through multi-dataflow fusion. Sampled Dense-Dense Matrix Multiplication (SDDMM) and Sparse-Dense Matrix Multiplication (SpMM) are core components of sparse attention mechanism. FCU unifies the computation ways of matrix inner product and row-wise product to support SDDMM and SpMM at the same time, which greatly reduces the storage and movement overhead of intermediate results. Lastly, we devise a lightweight buffer and data tiling strategy tailored to the proposed accelerator, aimed at enhancing data reuse. Experiments demonstrate that our accelerator achieves 0.10-0.25 sparsity with small accuracy loss. When computing the self-attention layer, it attains hardware efficiency ranging from 60% to 85%. Compared to CPU and GPU, it achieves 5.60x and 8.20x speedup. Compared to the state-of-the-art attention accelerators A 3 , SpAtten, FTRANS, and Sanger, it achieves 7.37x, 4.52x, 9.58x, and 3.08x speedup.

关键词： Quantization (signal) Graphics processing units computer architecture Transformer cores Transformers Prediction algorithms Hardware Spatiotemporal phenomena Sparse matrices Sorting

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：